Analyzing discontinuities in longitudinal count data: A multilevel generalized linear mixed model.

Peugh, James; Beal, Sarah J.; McGrady, Meghan E.; Toland, Michael D.; Mara, Constance A.

doi:10.1037/met0000347

Cited by 6 publications

(4 citation statements)

References 68 publications

(109 reference statements)

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“…According to whether the participant was finally diagnosed with AIS, we set AIS as “outcome variable,” and used the generalized linear model (GLM) algorithm, that is, establish the relationship between the mathematical expectation of the response variable and the prediction variable of the linear combination through the linkage function. [ 16 ] At the same time, in order to ensure the best combination of variables included in GLM, we adopted the variable screening mode based on Akaike Information Criterion (AIC), that is, AIC can compare the relative difference/distance between each alternative model and the “real model,” the smaller the AIC value, the shorter the distance between the alternative model and the “real model” (the smaller the information loss). [ 17 ]…”

Section: Methodsmentioning

confidence: 99%

Development and validation of machine learning-based models for prediction of adolescent idiopathic scoliosis: A retrospective study

Zheng

Wang

et al. 2023

Medicine

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Adolescent idiopathic scoliosis (AIS) can cause abnormal body posture, which has a negative impact on the overall posture. Therefore, timely prevention and early treatment are extremely important. The purpose of this study is to build an early warning model of AIS risk, so as to provide guidance for accurately identifying early high-risk AIS children and adolescents. We conducted a retrospective study of 1732 children and adolescents with or without AIS who underwent physical examination in Longgang District Central Hospital of Shenzhen (LDCHS queue) from January 2019 to October 2022 and 1581 children and adolescents with or without AIS in Shenzhen People Hospital (January 2018 to December 2022) as external validation queues (SPH queue). The random forest model (RFM), support vector machine model, artificial neural network model (ANNM), decision tree model (DTM), and generalized linear model (GLM) were used to build AIS model for children and adolescents. The predictive efficacy of 5 machine learning models was evaluated by receiver operating characteristic curve and decision curve analysis. For screening candidate predictors of AIS, the ratio of sitting height to standing height (ROSHTSH), angle of lumbar rotation, scapular tilt (ST), shoulder-height difference (SHD), lumbar concave (LC), pelvic tilt (PT) and angle of thoracolumbar rotation (AOTR) can be used as a potential predictor of AIS. The effectiveness of the prediction model constructed by the 5 machine learning algorithms was between (area under the curve [AUC]: 0.767, 95% confidence interval [CI]: 0.710–0.824) and (AUC: 0.899, 95% CI: 0.842–0.956) in the training set and internal verification set, respectively. Among them, the ANNM was equipped with the best prediction effectiveness (training set: AUC: 0.899, 95% CI: 0.842–0.956) and (internal verification set: AUC: 0.897, 95% CI: 0.842–0.952). The prediction model of AIS based on machine learning algorithm can achieve satisfactory prediction efficiency, among which ANNM is the best, which can be used to guide clinicians in diagnosis and treatment and improve the prognosis of AIS children and adolescents.

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Section: Methodsmentioning

confidence: 99%

Development and validation of machine learning-based models for prediction of adolescent idiopathic scoliosis: A retrospective study

Zheng

Wang

et al. 2023

Medicine

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“…Poisson, RIP, NB, ZIP, ZI-RIP, and ZI-NB were fitted to each type of offense data (offenses against a person, property offenses, and drugrelated offenses) separately. For each outcome variable, the model with the smallest Bayesian information criterion (BIC) evidenced the bestfitting model and would be retained for interpretation (Peugh et al, 2021). Building upon the unconditional DGM, we investigated the extent to which ICE accounted for variances of u i 0 , u i 1 , u i 2 , u i 3 , and when possible, λ ti * .…”

Section: Measurementmentioning

confidence: 99%

“…One explanation of the discrepant findings centers around the power and sample size. Moderation for ML-GLMM is notoriously underpowered to detect significant effects (Peugh et al, 2021). There were 138 neighborhoods included in the current study as compared to more than 500 communities involved in McCarthy et al (2021).…”

Section: The Enduring Impact Of the Covid-19 Pandemic On Youth Delinq...mentioning

confidence: 99%

Impact of the COVID-19 pandemic on youth delinquency: A discontinuous growth analysis

Quintana²,

Shelton³

et al. 2023

Front. Educ.

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IntroductionThis study aimed to investigate (a) the immediate and long-term changes in youth offending rates among 138 neighborhoods within a large metropolitan area in the context of COVID-19 and (b) the extent to which the socioeconomic composition of the neighborhoods accounted for variations of the changes.MethodsDiscontinuous growth models were applied to demonstrate the changes in offenses against a person, property offenses, and drug-related offenses one-year prior to, at (March 2020), and one-year following the pandemic.ResultsAt the onset of the pandemic, we registered an immediate reduction in offenses against a person and property offenses but not in drug-related offenses. There was a steeper declining trend for property offenses one-year following the pandemic as compared with that one-year prior to the pandemic. The neighborhood concentration of affluence and poverty was not related to the immediate reduction in any type of delinquency.DiscussionWe conclude that the COVID-19 pandemic not only had an abrupt but also an enduring impact on youth delinquency.

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“…Specifically, discontinuity LGMs would examine whether a growth trajectory shifted in level and/or slope after pandemic onset by using the timing of the pandemic to inform the analysis, yielding three possible discontinuity models that are presented below. For more resources about discontinuity models, including their estimation in multilevel modeling, see Grimm and Marcoulides (2016), Peugh et al (2020), and Singer and Willett (2003).…”

Section: Lgms To Estimate Pandemic-related Discontinuities In Developmentmentioning

confidence: 99%

Solutions for latent growth modeling following COVID-19-related discontinuities in change and disruptions in longitudinal data collection

Rioux

Stickley

Little

2021

International Journal of Behavioral Development

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Following the onset of the novel coronavirus disease 2019 (COVID-19) pandemic, daily life significantly changed for the population. Accordingly, researchers interested in examining patterns of change over time may now face discontinuities around the pandemic. Researchers collecting in-person longitudinal data also had to cancel or delay data collection waves, further complicating analyses. Accordingly, the purpose of this article is to aid researchers aiming to examine latent growth models (LGM) in analyzing their data following COVID-19. An overview of basic LGM notions, LGMs with discontinuities, and solutions for studies that had to cancel or delay data collection waves are discussed and exemplified using simulated data. Syntax for R and Mplus is available to readers in online supplemental materials.

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Analyzing discontinuities in longitudinal count data: A multilevel generalized linear mixed model.

Cited by 6 publications

References 68 publications

Development and validation of machine learning-based models for prediction of adolescent idiopathic scoliosis: A retrospective study

Development and validation of machine learning-based models for prediction of adolescent idiopathic scoliosis: A retrospective study

Impact of the COVID-19 pandemic on youth delinquency: A discontinuous growth analysis

Solutions for latent growth modeling following COVID-19-related discontinuities in change and disruptions in longitudinal data collection

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